Literature DB >> 31641905

External validation of a prediction model for pain and functional outcome after elective lumbar spinal fusion.

Ayesha Quddusi1, Hubert A J Eversdijk2, Anita M Klukowska2,3, Marlies P de Wispelaere4, Julius M Kernbach5, Marc L Schröder2, Victor E Staartjes6,7,8.   

Abstract

OBJECTIVE: Patient-reported outcome measures following elective lumbar fusion surgery demonstrate major heterogeneity. Individualized prediction tools can provide valuable insights for shared decision-making. We externally validated the spine surgical care and outcomes assessment programme/comparative effectiveness translational network (SCOAP-CERTAIN) model for prediction of 12-month minimum clinically important difference in Oswestry Disability Index (ODI) and in numeric rating scales for back (NRS-BP) and leg pain (NRS-LP) after elective lumbar fusion.
METHODS: Data from a prospective registry were obtained. We calculated the area under the curve (AUC), calibration slope and intercept, and Hosmer-Lemeshow values to estimate discrimination and calibration of the models.
RESULTS: We included 100 patients, with average age of 50.4 ± 11.4 years. For 12-month ODI, AUC was 0.71 while the calibration intercept and slope were 1.08 and 0.95, respectively. For NRS-BP, AUC was 0.72, with a calibration intercept of 1.02, and slope of 0.74. For NRS-LP, AUC was 0.83, with a calibration intercept of 1.08, and slope of 0.95. Sensitivity ranged from 0.64 to 1.00, while specificity ranged from 0.38 to 0.65. A lack of fit was found for all three models based on Hosmer-Lemeshow testing.
CONCLUSIONS: The SCOAP-CERTAIN tool can accurately predict which patients will achieve favourable outcomes. However, the predicted probabilities-which are the most valuable in clinical practice-reported by the tool do not correspond well to the true probability of a favourable outcome. We suggest that any prediction tool should first be externally validated before it is applied in routine clinical practice. These slides can be retrieved under Electronic Supplementary Material.

Entities:  

Keywords:  External validation; Lumbar fusion; Outcome prediction; Patient-reported outcome; Predictive analytics

Mesh:

Year:  2019        PMID: 31641905     DOI: 10.1007/s00586-019-06189-6

Source DB:  PubMed          Journal:  Eur Spine J        ISSN: 0940-6719            Impact factor:   3.134


  35 in total

1.  Assessing calibration in an external validation study.

Authors:  Gary S Collins; Emmanuel O Ogundimu; Yannick Le Manach
Journal:  Spine J       Date:  2015-11-01       Impact factor: 4.166

2.  Development and Validation of a Prediction Model for Pain and Functional Outcomes After Lumbar Spine Surgery.

Authors:  Sara Khor; Danielle Lavallee; Amy M Cizik; Carlo Bellabarba; Jens R Chapman; Christopher R Howe; Dawei Lu; A Alex Mohit; Rod J Oskouian; Jeffrey R Roh; Neal Shonnard; Armagan Dagal; David R Flum
Journal:  JAMA Surg       Date:  2018-07-01       Impact factor: 14.766

3.  Machine-Learning Models: The Future of Predictive Analytics in Neurosurgery.

Authors:  G Damian Brusko; John Paul G Kolcun; Michael Y Wang
Journal:  Neurosurgery       Date:  2018-07-01       Impact factor: 4.654

4.  Current Status of Worldwide Use of Patient-Reported Outcome Measures (PROMs) in Spine Care.

Authors:  Asdrubal Falavigna; Diego Cassol Dozza; Alisson R Teles; Chung Chek Wong; Giuseppe Barbagallo; Darrel Brodke; Abdulaziz Al-Mutair; Zoher Ghogawala; K Daniel Riew
Journal:  World Neurosurg       Date:  2017-09-08       Impact factor: 2.104

5.  Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study.

Authors:  Christiaan H B van Niftrik; Frank van der Wouden; Victor E Staartjes; Jorn Fierstra; Martin N Stienen; Kevin Akeret; Martina Sebök; Tommaso Fedele; Johannes Sarnthein; Oliver Bozinov; Niklaus Krayenbühl; Luca Regli; Carlo Serra
Journal:  Neurosurgery       Date:  2019-10-01       Impact factor: 4.654

6.  Revisions for screw malposition and clinical outcomes after robot-guided lumbar fusion for spondylolisthesis.

Authors:  Marc L Schröder; Victor E Staartjes
Journal:  Neurosurg Focus       Date:  2017-05       Impact factor: 4.047

7.  A clinical prediction model to assess surgical outcome in patients with cervical spondylotic myelopathy: internal and external validations using the prospective multicenter AOSpine North American and international datasets of 743 patients.

Authors:  Lindsay A Tetreault; Pierre Côté; Branko Kopjar; Paul Arnold; Michael G Fehlings
Journal:  Spine J       Date:  2014-12-27       Impact factor: 4.166

8.  Assessing the performance of prediction models: a framework for traditional and novel measures.

Authors:  Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan
Journal:  Epidemiology       Date:  2010-01       Impact factor: 4.822

9.  Patient-reported outcomes unbiased by length of follow-up after lumbar degenerative spine surgery: Do we need 2 years of follow-up?

Authors:  Victor E Staartjes; Alessandro Siccoli; Marlies P de Wispelaere; Marc L Schröder
Journal:  Spine J       Date:  2018-10-05       Impact factor: 4.166

Review 10.  Comparative Effectiveness and Economic Evaluations of Open Versus Minimally Invasive Posterior or Transforaminal Lumbar Interbody Fusion: A Systematic Review.

Authors:  Christina L Goldstein; Frank M Phillips; Y Raja Rampersaud
Journal:  Spine (Phila Pa 1976)       Date:  2016-04       Impact factor: 3.468

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  5 in total

1.  Development of a machine-learning based model for predicting multidimensional outcome after surgery for degenerative disorders of the spine.

Authors:  D Müller; D Haschtmann; T F Fekete; F Kleinstück; R Reitmeir; M Loibl; D O'Riordan; F Porchet; D Jeszenszky; A F Mannion
Journal:  Eur Spine J       Date:  2022-07-14       Impact factor: 2.721

2.  Validation of the ACS-NSQIP Risk Calculator: A Machine-Learning Risk Tool for Predicting Complications and Mortality Following Adult Spinal Deformity Corrective Surgery.

Authors:  Katherine E Pierce; Bhaveen H Kapadia; Sara Naessig; Waleed Ahmad; Shaleen Vira; Carl Paulino; Michael Gerling; Peter G Passias
Journal:  Int J Spine Surg       Date:  2021-12

3.  Prediction Models in Degenerative Spine Surgery: A Systematic Review.

Authors:  Daniel Lubelski; Andrew Hersh; Tej D Azad; Jeff Ehresman; Zachary Pennington; Kurt Lehner; Daniel M Sciubba
Journal:  Global Spine J       Date:  2021-04

4.  Robot-Guided Transforaminal Versus Robot-Guided Posterior Lumbar Interbody Fusion for Lumbar Degenerative Disease.

Authors:  Victor E Staartjes; Bianca Battilana; Marc L Schröder
Journal:  Neurospine       Date:  2020-12-14

5.  Machine learning in neurosurgery: a global survey.

Authors:  Victor E Staartjes; Vittorio Stumpo; Julius M Kernbach; Anita M Klukowska; Pravesh S Gadjradj; Marc L Schröder; Anand Veeravagu; Martin N Stienen; Christiaan H B van Niftrik; Carlo Serra; Luca Regli
Journal:  Acta Neurochir (Wien)       Date:  2020-08-18       Impact factor: 2.216

  5 in total

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